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Sustainable Decision-Making Enhancement: Trust and Linguistic-Enhanced Conflict Measurement in Evidence Theory

Author

Listed:
  • Qiang Liu

    (School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Qingmiao Liu

    (School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Minhuan Wang

    (School of Architecture and Urban Planning, Tongji University, Shanghai 200082, China)

Abstract

This research presents an advanced methodology to enhance conflict measurement within the Dempster–Shafer framework, integrating linguistic preferences and trust relationships for improved sustainability decision-making. By developing a unique algorithm, we introduce a novel approach to quantify inter-expert similarity and establish consensus thresholds. Furthermore, our study innovates with a dual-path adjustment mechanism to effectively reconcile discrepancies in expert opinions. These methodological advancements enable a more accurate and nuanced representation of expert judgments, facilitating superior decision support in sustainability-oriented applications. Through rigorous numerical simulations and a detailed case study, we validate our approach’s efficacy in optimizing decision-making processes, underscoring its potential to significantly influence sustainable practices and policy formulation. Our contributions not only advance theoretical understanding but also offer practical tools for incorporating expert insights into the pursuit of sustainability goals, marking a significant leap forward in decision-making research.

Suggested Citation

  • Qiang Liu & Qingmiao Liu & Minhuan Wang, 2024. "Sustainable Decision-Making Enhancement: Trust and Linguistic-Enhanced Conflict Measurement in Evidence Theory," Sustainability, MDPI, vol. 16(6), pages 1-25, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2288-:d:1354220
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    References listed on IDEAS

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    1. Tianxiang Zhan & Fuyuan Xiao, 2021. "A Fast Evidential Approach for Stock Forecasting," Papers 2104.05204, arXiv.org, revised Jul 2021.
    2. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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